Abstract:
Recent advances in computing, algorithmics and robotics have sparked discussion regarding the potential seismic impacts on many aspects of human life, including education. While the flow-on effects of automation have historically been confined to low-skill occupations, commentators are increasingly predicting that artificial intelligence (AI) will soon come to disrupt a range of more highly-skilled professions, such as teaching.
In this presentation we will synthesise some of the key threads in the emerging literature, and use this to ground a speculative inquiry into the implications of AI for teachers and teaching, but also for how we think about education more broadly.
We will outline aspects of both the optimistic and pessimistic prognoses for teaching and education, including implications for social justice. For example, it must be considered whether AI in education could serve to narrow educational gaps (by providing targeted, perfectly-timed learning for all students), or be a force for widening inequality (in terms of access to AI-enhanced learning, but also in regard to the potentially marginalising effect of algorithmically-normalised expectations).
As we move from specialised AI towards more general AI, we are led to the question of what will be left over for (human) teachers once the key technical and calculative tasks are taken over by computers. We will present some cautious responses to these questions, and reflect on the practices via which these most ‘human’ aspects of teaching may be explored and cultivated.
Overall, we will argue that the rise of AI incites a necessary and potentially invigorating return to basic questions in the philosophy of education. In this way, whatever the eventual impact of AI on teaching, its looming influence may be a catalyst for productive educational reflection.
In this presentation we will synthesise some of the key threads in the emerging literature, and use this to ground a speculative inquiry into the implications of AI for teachers and teaching, but also for how we think about education more broadly.
We will outline aspects of both the optimistic and pessimistic prognoses for teaching and education, including implications for social justice. For example, it must be considered whether AI in education could serve to narrow educational gaps (by providing targeted, perfectly-timed learning for all students), or be a force for widening inequality (in terms of access to AI-enhanced learning, but also in regard to the potentially marginalising effect of algorithmically-normalised expectations).
As we move from specialised AI towards more general AI, we are led to the question of what will be left over for (human) teachers once the key technical and calculative tasks are taken over by computers. We will present some cautious responses to these questions, and reflect on the practices via which these most ‘human’ aspects of teaching may be explored and cultivated.
Overall, we will argue that the rise of AI incites a necessary and potentially invigorating return to basic questions in the philosophy of education. In this way, whatever the eventual impact of AI on teaching, its looming influence may be a catalyst for productive educational reflection.